title: Neural network-based classification of anesthesia/awareness using Granger causality features.
abstract: This article investigates the signal processing part of a future system for monitoring awareness during surgery. The system uses features from the patients' electrical brain activity (EEG) to discriminate between "anesthesia" and "awareness." We investigate the use of a neural network classifier and Granger causality (GC) features for this purpose. GC captures anesthetic-induced changes in the causal relationships between pairs of signals from different brain areas. The differences in the pairwise causality estimated from the EEG activity are used as features for subsequent classification between "awake" and "anesthetized" states. EEG data from 31 subjects obtained during surgery and maintenance of anesthesia with propofol, sevoflurane, or desflurane, are classified using a neural network with one layer of hidden units. An average accuracy of 96% is obtained.
authors.affiliation: KIOS Research Centre, Department of Electrical and Computer Engineering, University of Cyprus, Nicosia, Cyprus.
authors.forename: Nicoletta
authors.initials: N
authors.lastname: Nicolaou
authors.forename: Julius
authors.initials: J
authors.lastname: Georgiou
mesh_terms: D000293:Adolescent; D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D000758:Anesthesia / Q000009:adverse effects / Q000379:methods; D004569:Electroencephalography / Q000379:methods; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D008962:Models, Theoretical; D009415:Nerve Net / Q000503:physiopathology*; D014851:Wakefulness; D055815:Young Adult
keywords: anesthesia; awareness; electroencephalogram; granger causality; neural network classifier